{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code to generate GoogLeNet class visualizations in the shape of a clipping mask. See [this blogpost](http://auduno.com/post/125837418083/drawing-with-googlenet) for details.\n",
    "\n",
    "Before running the code, insert your pycaffe path in ```pycaffe_root``` and insert the path to the googlenet model in ```model_path```. Download the bvlc googlenet model from [here](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) if you haven't done so already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# imports and basic notebook setup\n",
    "from cStringIO import StringIO\n",
    "import numpy as np\n",
    "import os,re,random\n",
    "import scipy.ndimage as nd\n",
    "import PIL.Image\n",
    "import sys,string,math,time\n",
    "from IPython.display import clear_output, Image, display\n",
    "from google.protobuf import text_format\n",
    "from scipy.misc import imresize\n",
    "\n",
    "pycaffe_root = \"/your/path/here/caffe/python\" # substitute your path here\n",
    "sys.path.insert(0, pycaffe_root)\n",
    "import caffe\n",
    "\n",
    "model_name = \"GoogLeNet\"\n",
    "model_path = '/your/path/here/caffe_models/bvlc_googlenet/' # substitute your path here\n",
    "net_fn   = '../deploy_googlenet_updated.prototxt'\n",
    "param_fn = model_path + 'bvlc_googlenet.caffemodel'\n",
    "mean = np.float32([104.0, 117.0, 123.0])\n",
    "\n",
    "#caffe.set_mode_gpu() # uncomment this if gpu processing is available\n",
    "net = caffe.Classifier(net_fn, param_fn,\n",
    "                       mean = mean, # ImageNet mean, training set dependent\n",
    "                       channel_swap = (2,1,0)) # the reference model has channels in BGR order instead of RGB\n",
    "\n",
    "# a couple of utility functions for converting to and from Caffe's input image layout\n",
    "def preprocess(net, img):\n",
    "    return np.float32(np.rollaxis(img, 2)[::-1]) - net.transformer.mean['data']\n",
    "def deprocess(net, img):\n",
    "    return np.dstack((img + net.transformer.mean['data'])[::-1])\n",
    "\n",
    "def blur(img, sigma):\n",
    "    if sigma > 0:\n",
    "        img[0] = nd.filters.gaussian_filter(img[0], sigma, order=0)\n",
    "        img[1] = nd.filters.gaussian_filter(img[1], sigma, order=0)\n",
    "        img[2] = nd.filters.gaussian_filter(img[2], sigma, order=0)\n",
    "    return img\n",
    "\n",
    "def showarray(a, f, fmt='jpeg'):\n",
    "    a = np.uint8(np.clip(a, 0, 255))\n",
    "    f = StringIO()\n",
    "    PIL.Image.fromarray(a).save(f, fmt)\n",
    "    display(Image(data=f.getvalue()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_step(net, step_size=1.5, end='inception_4c/output', clip=True, focus=None, sigma=None):\n",
    "    '''Basic gradient ascent step.'''\n",
    "\n",
    "    src = net.blobs['data'] # input image is stored in Net's 'data' blob\n",
    "\n",
    "    dst = net.blobs[end]\n",
    "    net.forward(end=end)\n",
    "\n",
    "    one_hot = np.zeros_like(dst.data)\n",
    "    one_hot.flat[focus] = 1.\n",
    "    dst.diff[:] = one_hot\n",
    "\n",
    "    net.backward(start=end)\n",
    "    g = src.diff[0]\n",
    "    \n",
    "    src.data[:] += step_size/np.abs(g).mean() * g\n",
    "\n",
    "    if clip:\n",
    "        bias = net.transformer.mean['data']\n",
    "        src.data[:] = np.clip(src.data, -bias, 255-bias) \n",
    "        \n",
    "    src.data[0] = blur(src.data[0], sigma)\n",
    "    \n",
    "    dst.diff.fill(0.)\n",
    "\n",
    "def deepdraw(net, base_img, octaves, focus, visualize=True, clip=True, clip_arr=None):\n",
    "    \n",
    "    # prepare base image\n",
    "    image = preprocess(net, base_img) # (3,224,224)\n",
    "    \n",
    "    # get input dimensions from net\n",
    "    w = net.blobs['data'].width\n",
    "    h = net.blobs['data'].height\n",
    "    \n",
    "    print \"starting drawing\"\n",
    "    src = net.blobs['data']\n",
    "    src.reshape(1,3,h,w) # resize the network's input image size\n",
    "    current_scale = 1.0\n",
    "    for e,o in enumerate(octaves):\n",
    "        if 'scale' in o:\n",
    "            image = nd.zoom(image, (1,o['scale'],o['scale']))\n",
    "            current_scale *= o['scale']\n",
    "            # scale clip_mask as well\n",
    "            clip_arr = np.abs(np.round(nd.zoom(clip_arr, (o['scale'],o['scale']))))\n",
    "        _,imw,imh = image.shape\n",
    "        \n",
    "        # select layer\n",
    "        layer = o['layer']\n",
    "\n",
    "        for i in xrange(o['iter_n']):\n",
    "            # randomly select a 224x224 crop\n",
    "            ox = random.randint(0,imw-224)\n",
    "            oy = random.randint(0,imh-224)\n",
    "\n",
    "            # insert the crop into src.data[0]\n",
    "            src.data[0] = image[:,ox:ox+224,oy:oy+224]\n",
    "\n",
    "            sigma = o['start_sigma'] + ((o['end_sigma'] - o['start_sigma']) * i) / o['iter_n']\n",
    "            step_size = o['start_step_size'] + ((o['end_step_size'] - o['start_step_size']) * i) / o['iter_n']\n",
    "\n",
    "            make_step(net, end=layer, clip=clip, focus=focus, sigma=sigma, step_size=step_size)\n",
    "\n",
    "            if visualize:\n",
    "                vis = deprocess(net, src.data[0])\n",
    "                if not clip: # adjust image contrast if clipping is disabled\n",
    "                    vis = vis*(255.0/np.percentile(vis, 99.98))\n",
    "                showarray(vis,\"./filename\"+str(i)+\"_\"+str(f)+\".jpg\")\n",
    "\n",
    "            if i > 0 and i % 10 == 0:\n",
    "                print 'finished step %d in octave %d' % (i,e)\n",
    "\n",
    "            # insert modified image into image\n",
    "            image[:,ox:ox+224,oy:oy+224] = src.data[0]\n",
    "\n",
    "            # apply clipping mask\n",
    "            if not clip_arr is None:\n",
    "                image *= clip_arr\n",
    "                image[np.where(np.tile(clip_arr,(3,1,1)) == 0.)] = 255.\n",
    "        \n",
    "        print \"octave %d image:\" % e\n",
    "        showarray(deprocess(net, image),\"./octave_\"+str(e)+\".jpg\")\n",
    "            \n",
    "    # returning the resulting image\n",
    "    return deprocess(net, image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We load the clipping mask as black and white image and convert it to a numeric clipping mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting drawing\n",
      "finished step 10 in octave 0\n",
      "finished step 20 in octave 0\n",
      "finished step 30 in octave 0\n",
      "finished step 40 in octave 0\n",
      "finished step 50 in octave 0\n",
      "finished step 60 in octave 0\n",
      "finished step 70 in octave 0\n",
      "finished step 80 in octave 0\n",
      "finished step 90 in octave 0\n",
      "finished step 100 in octave 0\n",
      "finished step 110 in octave 0\n",
      "finished step 120 in octave 0\n",
      "finished step 130 in octave 0\n",
      "finished step 140 in octave 0\n",
      "finished step 150 in octave 0\n",
      "finished step 160 in octave 0\n",
      "finished step 170 in octave 0\n",
      "finished step 180 in octave 0\n",
      "octave 0 image:\n"
     ]
    },
    {
     "data": {
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7NdgjGW3dXrnLuK2kVYri+f7Zk+WmMAgDpXUkpKKTPXhJSpt2OpV7hLuZrZlV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\nbtnVUpu7TjZGMbeGQ+arM2z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G8XzFad5c/eOztipYl2qM9W5pZUFvbfNku33RRBuYgkcA\nc8VabcbvoZuUI7FvyTlQOvU1sW6bIQO5rMtN8rrjIHfitiok7grW0CiiioGFFFFABRRRQAUUUUAF\nFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZF9GQzAde1ZspSOMq2N79FHrW9fKvlh2BIHpWCI980ly8\neNp2xrnk1onoaXTV2V3QxL5aKN38VKjeQwUEbz1x2q00X+kR/L94c+1VQEaeQhc5OAaTpp6voYS5\ndL7lhme5haNAdvegQnygicAdTVi2/dEKR1GCBUksWIwOVBPJqoydrFuKvoQfZt8ZZTnsRVF7Y7jx\n+FbSRlMNglTUc8TPLnbjjjjrRGTTsEndEFnFsxlRjtV6bEiozLtC/wARqO1EaOCw5HUelJd7pG6j\naD0PFS9XsHKtFIuWTDBGetXaoWqssYJbOelXgdwyKlhp0GOQ3Q9KiZMjjk05jhsetGeM+xNJC6lf\nyjvBJ57U5wTwQRipwc7T6gGgcpk9ad76sLFcj5QCOaNuRg1OQAoOOtRk84wKT1Q1pqivJGo4GCar\nrCQ2T61dkjXGe9NjUNkU+gvNlWVFkYE9V6VG1tI4IVsdxirEiASYqxFGAOpq9YrQLLew+yjKrluu\nKt02MYQU6swSS2CiiigYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAyWMSRlT9\nazfLWJyxHQ5rVqu0aszA9M4ppgY9wjuwOdoYdqdFZ/KB0UenWtIwI6DI6dMUvkrGyqCcGrcroSVr\nJFKSJ44c2+DL/FnqRUiKFiwckHnDetWXjVAGHJ96eY1z+GazhJ2sxvfQqjIUJ2qVC2fmwfTNSMig\nKfWgoMZ9s0wZCsarIzEDLU24txOUOBx0qcqFRW7k/wBDUhUbsepxRcb7lSON1k+bP0q7Ecrg9aQq\nMgc+tJztHJobuDdz/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished step 10 in octave 1\n",
      "finished step 20 in octave 1\n",
      "finished step 30 in octave 1\n",
      "finished step 40 in octave 1\n",
      "finished step 50 in octave 1\n",
      "finished step 60 in octave 1\n",
      "finished step 70 in octave 1\n",
      "finished step 80 in octave 1\n",
      "finished step 90 in octave 1\n",
      "finished step 100 in octave 1\n",
      "finished step 110 in octave 1\n",
      "finished step 120 in octave 1\n",
      "finished step 130 in octave 1\n",
      "finished step 140 in octave 1\n",
      "octave 1 image:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/scipy/ndimage/interpolation.py:532: UserWarning: From scipy 0.13.0, the output shape of zoom() is calculated with round() instead of int() - for these inputs the size of the returned array has changed.\n",
      "  \"the returned array has changed.\", UserWarning)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished step 10 in octave 2\n",
      "finished step 20 in octave 2\n",
      "finished step 30 in octave 2\n",
      "finished step 40 in octave 2\n",
      "finished step 50 in octave 2\n",
      "finished step 60 in octave 2\n",
      "finished step 70 in octave 2\n",
      "finished step 80 in octave 2\n",
      "finished step 90 in octave 2\n",
      "finished step 100 in octave 2\n",
      "finished step 110 in octave 2\n",
      "finished step 120 in octave 2\n",
      "finished step 130 in octave 2\n",
      "finished step 140 in octave 2\n",
      "octave 2 image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "octave 3 image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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wvXFYTSctUVDE1IvlMCCa4uAEMRQrxzwP89Km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\n5m8qTa/opyDj61bs5MSFCSiZ4Ws6G1YXIDrlc81bmRflUZLZ4weldMY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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "took seconds : 727.755947\n"
     ]
    }
   ],
   "source": [
    "# get clipping mask\n",
    "clip_img = PIL.Image.open(\"./clipping_masks/clipping_mask_a.png\")\n",
    "clip_arr = np.asarray(clip_img.convert(\"L\"))\n",
    "clip_arr = 1. - clip_arr/255.\n",
    "clip_arr = np.round(clip_arr)\n",
    "\n",
    "octaves = [\n",
    "    {\n",
    "        'layer':'loss3/classifier',\n",
    "        'iter_n':190,\n",
    "        'start_sigma':2.5,\n",
    "        'end_sigma':0.78,\n",
    "        'start_step_size':12.,\n",
    "        'end_step_size':12.\n",
    "    },\n",
    "    {\n",
    "        'layer':'loss3/classifier',\n",
    "        'scale':1.2,\n",
    "        'iter_n':150,\n",
    "        'start_sigma':0.78*1.2,\n",
    "        'end_sigma':0.78,\n",
    "        'start_step_size':6.,\n",
    "        'end_step_size':6.\n",
    "    },\n",
    "    {\n",
    "        'layer':'loss2/classifier',\n",
    "        'scale':1.2,\n",
    "        'iter_n':150,\n",
    "        'start_sigma':0.78*1.2,\n",
    "        'end_sigma':0.44,\n",
    "        'start_step_size':6.,\n",
    "        'end_step_size':3.\n",
    "    },\n",
    "    {\n",
    "        'layer':'loss1/classifier',\n",
    "        'scale':1.0,\n",
    "        'iter_n':10,\n",
    "        'start_sigma':0.44,\n",
    "        'end_sigma':0.304,\n",
    "        'start_step_size':3.,\n",
    "        'end_step_size':3.\n",
    "    }\n",
    "]\n",
    "\n",
    "focus = 368 # capuchins\n",
    "\n",
    "start_dim = [clip_arr.shape[0],clip_arr.shape[1]]\n",
    "background_color = np.float32([250.0, 250.0, 250.0])\n",
    "gen_image = np.random.normal(background_color, 8, (start_dim[0], start_dim[1], 3))\n",
    "\n",
    "starttime = time.time()\n",
    "gen_image = deepdraw(net, gen_image, octaves, focus, visualize=False, clip_arr=clip_arr)\n",
    "print \"took seconds : %f\" % (time.time()-starttime)\n",
    "\n",
    "# save image\n",
    "#img_fn = '_'.join([model_name, \"deepdraw_clipping_mask\", str(focus)+'.png'])\n",
    "#PIL.Image.fromarray(np.uint8(gen_image)).save('./' + img_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
